International Research Journal of Education and Technology

Generative Artificial Intelligence (AI) and Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) by enabling machines to generate human-like text, understand context, and perform a wide range of language-based tasks. These models, powered by deep learning architectures such as transformers, are trained on vast datasets to generate coherent and contextually relevant text, making them useful in applications like content creation, chatbots, code generation, and automated summarization. This paper explores the architecture, training methodologies, and key challenges associated with LLMs, including computational costs, ethical concerns, biases, and misinformation risks. We also discuss advancements in model fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) to enhance their accuracy and applicability. Furthermore, we examine the real-world impact of generative AI in industries such as healthcare, education, and software development while addressing ongoing research directions for making these models more efficient, explainable, and aligned with human values.

Keywords: Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP)